9 citations · 28 across the 15 of their papers we have counts for
16 papers
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +3
The performance of machine learning and deep learning models largely depends on the quality of the training data. However, the quality of the real-world datasets is often compromis…
Noise-Aware Framework for Correcting Corrupted Labels
Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4
High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can sever…
Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
Phong Lam, Ha-Linh Nguyen, Thu-Trang Nguyen +2
High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-prone. Programmatic labeling a…
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
Dat Le, Duc-Cuong Le, Anh-Son Nguyen +4
Automated Machine Learning (AutoML) has improved access to machine learning, yet existing techniques often remain limited in flexibility, transparency, and execution reliability. C…
Model-Agnostic Correctness Assessment for LLM-Generated Code via Dynamic Internal Representation Selection
Thanh Trong Vu, Tuan-Dung Bui, Thu-Trang Nguyen +2
Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and are increasingly integrated into the software development process. However, ensuring t…
CABENCH: Benchmarking Composable AI for Solving Complex Tasks through Composing Ready-to-Use Models
Tung-Thuy Pham, Duy-Quan Luong, Minh-Quan Duong +4
Composable AI offers a scalable and effective paradigm for tackling complex AI tasks by decomposing them into sub-tasks and solving each sub-task using ready-to-use well-trained mo…
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
Tuan-Dung Bui, Thanh Trong Vu, Thu-Trang Nguyen +2
Ensuring the correctness of code generated by Large Language Models (LLMs) presents a significant challenge in AI-driven software development. Existing approaches predominantly rel…
Generating Critical Scenarios for Testing Automated Driving Systems
Trung-Hieu Nguyen, Truong-Giang Vuong, Hong-Nam Duong +4
Autonomous vehicles (AVs) have demonstrated significant potential in revolutionizing transportation, yet ensuring their safety and reliability remains a critical challenge, especia…
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
Tuan-Dung Bui, Duc-Thieu Luu-Van, Thanh-Phat Nguyen +3
Code completion is essential in software development, helping developers by predicting code snippets based on context. Among completion tasks, Method Body Completion (MBC) is parti…
An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
Thu-Trang Nguyen, Thanh Trong Vu, Hieu Dinh Vo +1
Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks, increasing the application of code LLMs in softw…
Automated Description Generation for Software Patches
Thanh Trong Vu, Tuan-Dung Bui, Thanh-Dat Do +3
Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aidin…
ARIST: An Effective API Argument Recommendation Approach
Son Nguyen, Cuong Tran Manh, Kien T. Tran +4
Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the rig…
Code-centric Learning-based Just-In-Time Vulnerability Detection
Son Nguyen, Thu-Trang Nguyen, Thanh Trong Vu +3
Attacks against computer systems exploiting software vulnerabilities can cause substantial damage to the cyber-infrastructure of our modern society and economy. To minimize the con…
Ranking Warnings of Static Analysis Tools Using Representation Learning
Kien-Tuan Ngo, Dinh-Truong Do, Thu-Trang Nguyen +1
Static analysis tools are frequently used to detect potential vulnerabilities in software systems. However, an inevitable problem of these tools is their large number of warnings w…
A Variability Fault Localization Approach for Software Product Lines
Thu-Trang Nguyen, Kien-Tuan Ngo, Son Nguyen +1
Software fault localization is one of the most expensive, tedious, and time-consuming activities in program debugging. This activity becomes even much more challenging in Software…
Variability Fault Localization: A Benchmark
Kien-Tuan Ngo, Thu-Trang Nguyen, Son Nguyen +1
Software fault localization is one of the most expensive, tedious, and time-consuming activities in program debugging. This activity becomes even much more challenging in Software…